Created for data scientists and ML engineers, the FeatureByte platform radically simplifies the entire feature lifecycle, while giving you total control.
AI data management is different
The old way of building and maintaining feature pipelines takes manual effort. Too much, if you ask us. Most data management tools simply aren’t made for data science workflows. That’s why we built a simple, yet industrialized way to create, experiment, serve and manage features, way faster.
ML feature lifecycle
Feature engineering and management doesn’t have to be complicated. Take charge of the entire ML feature lifecycle, integrating FeatureByte with your existing data warehouse or data lake and ML tools.
- Automatically generate relevant features using FeatureByte Copilot
- Create and share your own state-of-the-art features effortlessly
- Search and reuse features to create feature lists tailored to your use case
- Immediately access historical features through automated backfilling - let FeatureByte handle the complexity of time-aware SQL and Spark
- Experiment on live data at scale, innovating faster
- Iterate rapidly with different feature lists to create more accurate models
- Deploy AI data pipelines and serve features in minutes, including low-latency features
- Reduce costs and security risk by performing computations in your existing data platform
- Ensure data consistency between model training and inferencing
- Reduce cost and complexity by controlling feature explosion and usage
- Monitor the health of feature pipelines centrally
- Improve feature governance and security with role-based access control and approval workflows
The FeatureByte platform takes AI data far beyond the feature store, helping you manage the entire ML feature lifecycle at scale.
The FeatureByte Feature Platform
Self Organizing Catalog
CLOUD DATA PLATFORM
Observability & Governance
Create features declaratively with a Python SDK. Spend time ideating and incorporating domain information rather than writing and maintaining boilerplate code.